Harvard University is offering free online course on Introduction to Linear Models and Matrix Algebra. In this introductory data analysis course, University will use matrix algebra to represent the linear models that commonly used to model differences between experimental units.

In this four week course, applicants will learn to use R programming to apply linear models to analyze data in life sciences. This course will start on July 1, 2017.

Providers’ Details

Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge.

About This Course

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures.

Why Take This Course?

Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data.

Learning Outcomes

Matrix algebra notation

Matrix algebra operations

Application of matrix algebra to data analysis

Linear models

Brief introduction to the QR decomposition

Instructors

Rafael Irizarry

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data.

Michael Love

Michael Love is a postdoctoral fellow with Dr. Irizarry in the Department of Biostatistics at the Dana Farber Cancer Institute and Harvard T.H. Chan School of Public Health.

Requirements

How To Join This Course

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